01 · FRAME
Make the decision visible
Begin with a real engineering dilemma so students know what the knowledge is for.
Course FrameworkIn-class learning and after-class reasoning
Learning OutcomesExplicit learning outcomes mapped to evidence and readiness
Engineering FirstProblems, mechanisms, trade-offs and defensible decisions
Evidence DrivenStudents demonstrate capability rather than recall alone
Teaching philosophy
Start with a consequential problem. End with evidence of capability.
My teaching approach treats a course as an operating system for learning. Each week begins with an ill-structured engineering problem, establishes the domain spine, teaches the mechanisms that make the problem solvable, exposes common misconceptions, then asks students to produce evidence that they can act.
02 · UNDERSTAND
Teach mechanisms, not slogans
Build the technical spine: architecture, failure modes, constraints, measures and trade-offs.
03 · TEST
Surface misconceptions
Use polls, pause-and-discuss prompts, cases and collaborative work to make weak reasoning visible.
04 · EVIDENCE
Require a defensible artifact
Students finish by explaining, building, testing or defending a choice against explicit criteria.
COURSE · 01
CPIT-455 — Software Engineering II
A senior software-engineering course centered on reliability, safety, security, resilience, reuse, distributed systems and systems of systems.
Visit course site →iSCARB
iSCARB — defensible engineering judgment
When AI can generate an answer, students still need to explain why it is justified and when it stops being justified. iSCARB is implemented across nine software-engineering chapters and assignments, connecting a choice to its assumptions, boundaries, action and evidence. From Chapter 12, story-led lectures turn each chapter into a decision case, supported by executable labs, a two-minute ownership check and an AI study coach: AI is the practice layer, and engineering judgment is what is assessed.
FIT · BOUND · ACT · EVIDENCE, followed by STRESS and REFIT: change a constraint, then retain or revise the decision. Commitment before disclosure, declared AI use and sampled oral verification keep ownership with the learner.
Open preprint · 25 September 2026 · Not yet peer reviewed. The implementation is available to inspect; learning effectiveness remains a research question.
IMAM · iSCARB
IMAM within iSCARB
IMAM is the optional Saudi and Islamic contextualization layer within iSCARB. It connects relevant context, values and purpose to the same evidence and technical-fidelity requirements. It is presented as part of this teaching program.
The teaching design includes NELC alignment and a proposed Jaheziah readiness mapping for review and piloting.
BRIDGE · 04
Research-to-classroom
Research questions become teaching cases, and teaching failures become research questions. The boundary between scholarship and course design is deliberately porous.
What students practice
Engineering judgment under constraints.
The objective is not to memorize a definition of reliability, safety or security. It is to know what evidence changes the decision and what compromise the system can tolerate.
ExplainTranslate technical mechanisms into a defensible engineering argument.
DesignChoose architecture and controls against explicit requirements.
TestEvaluate failure modes, assumptions and evidence.
DefendJustify a decision when every option carries trade-offs.
Interested in curriculum design, educational AI or teaching collaboration?
I collaborate on higher-education readiness, engineering education, GenAI curriculum systems and evidence-centered learning design.